3D hand shape and pose estimation

ABSTRACT

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for receiving a monocular image that includes a depiction of a hand and extracting features of the monocular image using a plurality of machine learning techniques. The program and method further include modeling, based on the extracted features, a pose of the hand depicted in the monocular image by adjusting skeletal joint positions of a three-dimensional (3D) hand mesh using a trained graph convolutional neural network (CNN); modeling, based on the extracted features, a shape of the hand in the monocular image by adjusting blend shape values of the 3D hand mesh representing surface features of the hand depicted in the monocular image using the trained graph CNN; and generating, for display, the 3D hand mesh adjusted to model the pose and shape of the hand depicted in the monocular image.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/210,927, filed on Dec. 5, 2018, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to generating athree-dimensional (3D) model of a hand and more particularly toestimating a hand shape and pose for generating a 3D model using machinelearning.

BACKGROUND

The popularity of virtual reality (VR) and augmented reality (AR)applications continues to grow, and vision-based 3D hand analysis isvery important in this growing field. Particularly, these applicationsgenerally present an animated model of a hand in VR or AR whichrepresents the user's hand in the real world. The user interacts with VRor AR content using the animated model of the hand. In order to allowthe user to accurately position the animated model of the hand withinthe VR or AR application to interact with the VR or AR content,estimations of the real-world hand have to be performed accurately andquickly.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some embodiments are illustratedby way of example, and not limitation, in the figures of theaccompanying drawings in which:

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network,according to example embodiments.

FIG. 2 is a schematic diagram illustrating data which may be stored inthe database of a messaging server system, according to exampleembodiments.

FIG. 3 is a schematic diagram illustrating a structure of a messagegenerated by a messaging client application for communication, accordingto example embodiments.

FIG. 4 is a block diagram showing an example hand shape and poseestimation system, according to example embodiments.

FIG. 5 is a block diagram showing a 3D hand mesh generation process,according to example embodiments.

FIGS. 6-7 are flowcharts illustrating example operations of the handshape and pose estimation system, according to example embodiments.

FIG. 8 is an illustrative input and output of the hand shape and poseestimation system, according to example embodiments.

FIG. 9 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described, according to example embodiments.

FIG. 10 is a block diagram illustrating components of a machine able toread instructions from a machine-readable medium (e.g., amachine-readable storage medium) and perform any one or more of themethodologies discussed herein, according to example embodiments.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments. It will be evident, however, to those skilled in the art,that embodiments may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Typically, VR and AR systems display a 3D hand representation of areal-world user's hand by focusing on sparse 3D hand joint locations andignore dense 3D hand shapes. Specifically, these systems capture animage using a red, green, blue (RGB) camera and determine the jointpositions of the hand from the image. Given the diversity and complexityof real-world hand shapes, such typical systems simply obtain a generic3D hand model and use the determined joint positions to fit the obtainedgeneric hand model to resemble the joint positions (e.g., the fingerpositions) of the real-world hand. Such generic representations of thehand fail to consider the shape of the hand and other surface featuresof the hand, so the 3D hand model that is presented is not veryaccurate. This makes user interactions with content in the VR and ARsystems more difficult and less realistic, which detracts from theoverall user experience.

Some conventional systems improve the accuracy of the 3D hand model byobtaining a depth map using a depth sensor in addition to the RGB imageof the real-world hand. Particularly, such systems fit a deformable handmodel to the input depth map with iterative optimizations. The pose andshape parameters are obtained from the depth map using neural networks(e.g., a convolutional neural network (CNN)) and a 3D hand mesh isrecovered using a model. However, in such systems, the quality of therecovered hand mesh is restricted by the parameters of the model,meaning the generated 3D hand is not an entirely accurate representationof the user's hand. Also, adding depth sensors to user devices increasesthe overall cost and complexity of the devices, making them lessattractive.

The disclosed embodiments improve the efficiency of using the electronicdevice by applying machine learning techniques, including a graph CNN,to generate a 3D hand mesh for presentation in a VR or AR application.The 3D hand mesh is generated directly from a single RGB image depictinga real-world hand and represents the pose (e.g., joint locations) andshape (e.g., surface features) of the hand that is depicted in the RGBimage. The disclosed embodiments generate the 3D hand mesh without alsoobtaining a depth map of the real-world hand. Specifically, according tothe disclosed techniques, image features of a single RGB image areextracted by one or more machine learning techniques and then graphconvolutions (e.g., using a graph CNN) are applied hierarchically withupsampling and nonlinear activations to generate 3D hand mesh vertices.According to the disclosed embodiments, the generated 3D hand mesh canbetter represent highly variable 3D hand shapes (e.g., surface features)and their local details. This enables a user device with a simple RGBcamera (without a depth camera) to accurately and quickly render ananimated 3D hand model of the real-world user's hand within the VR or ARapplication, allowing the user to interact with the VR or AR content ina more realistic environment.

In some embodiments, the machine learning techniques generate the 3Dhand mesh directly from a single RGB image of the real-world hand afterbeing trained in two training phases. In a first of the two trainingphases, the machine learning techniques are trained using a syntheticdataset (e.g., animations of a hand) that include various ground truth3D hand information. In a second of the two training phases, the machinelearning techniques are trained using an RGB image depicting areal-world hand and a corresponding depth map together with a pseudo-3Dhand mesh generated by the machine learning techniques trained in thefirst training phase. After being trained in the two training phases,the machine learning techniques can generate the 3D hand mesh of thehand depicted in an RGB image received from a user device withoutobtaining depth information.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network106. The messaging system 100 includes multiple client devices 102, eachof which hosts a number of applications, including a messaging clientapplication 104 and a AR/VR application 105. Each messaging clientapplication 104 is communicatively coupled to other instances of themessaging client application 104, the AR/VR application 105, and amessaging server system 108 via a network 106 (e.g., the Internet).

Accordingly, each messaging client application 104 and AR/VR application105 is able to communicate and exchange data with another messagingclient application 104 and AR/VR application 105 and with the messagingserver system 108 via the network 106. The data exchanged betweenmessaging client applications 104, AR/VR applications 105, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

AR/VR application 105 is an application that includes a set of functionsthat allow the client device 102 to access hand shape and poseestimation system 124. In some implementations, the AR/VR application105 is a component or a feature that is part of the messaging clientapplication 104. AR/VR application 105 uses an RGB camera to capture amonocular image of a user's real-world hand. The AR/VR application 105applies various trained machine learning techniques on the capturedimage of the hand to generate a 3D hand model representation of the handthat includes the pose (e.g., the joint positions) and the shape (e.g.,the surface features and textures) of the hand. In some implementations,the AR/VR application 105 continuously captures images of the user'shand in real-time or periodically to continuously or periodically updatethe generated 3D hand model representation. The allows the user to movethe hand around in the real world and see the 3D hand model update inreal time to represent the user's hand moving around in the AR or VRenvironment. The AR/VR application 105 presents various content (e.g.,messages, games, advertisements, and so forth) and allows the user toposition the 3D hand model in the AR/R application 105 over the contentthat is presented by moving the user's hand in the real world. Once the3D hand model is positioned at a desired location, the user can performan action or gesture to make a selection of the content over which thehand is positioned.

In order for AR/VR application 105 to generate the 3D hand modeldirectly from a captured RGB image, the AR/VR application 105 obtainsone or more trained machine learning techniques from the hand shape andpose estimation system 124 and/or messaging server system 108. Handshape and pose estimation system 124 trains the machine learningtechniques to generate the 3D hand model in two training phases. In thefirst training phase, the hand shape and pose estimation system 124obtains a first plurality of input images that include syntheticrepresentations of a hand. These synthetic representations include adepiction of an animated hand and also provide the ground truthinformation about the shape and pose of the animated hand (e.g., a 3Dmesh and 3D pose ground truth information). A first machine learningtechnique (e.g., a two-stacked hourglass network) is initially trainedbased on a first feature (e.g., a heat-map loss) of the first pluralityof images. A second machine learning technique (e.g., a 3D poseregressor network) is initially trained, separately from the firstmachine learning technique, based on a second feature (e.g., a 3D poseloss) of the first plurality of images. The first and second machinelearning techniques are then trained together with a graph CNN based onthe first plurality of images with the combined mesh, pose and heat-maplosses. In the second training phase, a second plurality of input imagesare obtained that include real-world depictions of a hand and reference3D depth maps captured using a depth camera or sensor. A pseudo-groundtruth mesh of the real-world depictions of the hand is generated usingthe graph CNN trained in the first training phase. The first and secondmachine learning techniques are then trained together with the graph CNNbased on the pseudo-ground truth mesh, the real-world depictions of thehand, and the reference 3D depth maps. In some implementations, in thesecond training phase, the first machine learning technique (e.g., thestacked hourglass network) is first trained with the first feature ofthe second plurality of images (e.g., the heat-map loss) and then allthe machine learning techniques are trained or fine-tuned based on aheat-map loss, a depth loss, and a mesh loss.

For example, the input RGB image depicting a computer-generated handpose is passed through a two-stacked hourglass network for 2D hand poseestimation. The estimated 2D heat maps, combined with the image featuremaps, are encoded as latent feature vectors by a residual network. Thelatent feature vector is then input to a graph CNN to infer the 3Dcoordinates of the mesh vertices. Finally, the 3D hand pose is linearlyregressed from the 3D hand mesh. Specifically, the machine learningtechniques are initially trained on the synthetic dataset in a fullysupervised manner with heat-map loss, 3D mesh loss, and 3D pose loss.The machine learning techniques are then optimized using real-worlddatasets that include a depth map without 3D mesh or 3D pose groundtruth information. Particularly, the networks are fine-tuned in aweakly-supervised manner by rendering the full 3D hand mesh to a depthmap and minimizing the depth map loss against the reference depth map.These processes are described in more detail below in connection withFIGS. 6 and 7.

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality either within the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, but to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, virtual objects, messagecontent persistence conditions, social network information, and liveevent information, as examples. Data exchanges within the messagingsystem 100 are invoked and controlled through functions available viauser interfaces (UIs) of the messaging client application 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

Dealing specifically with the API server 110, this server 110 receivesand transmits message data (e.g., commands and message payloads) betweenthe client device 102 and the application server 112. Specifically, theAPI server 110 provides a set of interfaces (e.g., routines andprotocols) that can be called or queried by the messaging clientapplication 104 in order to invoke functionality of the applicationserver 112. The API server 110 exposes various functions supported bythe application server 112, including account registration; loginfunctionality; the sending of messages, via the application server 112,from a particular messaging client application 104 to another messagingclient application 104; the sending of media files (e.g., images orvideo) from a messaging client application 104 to the messaging serverapplication 114, and for possible access by another messaging clientapplication 104; the setting of a collection of media data (e.g.,story); the retrieval of such collections; the retrieval of a list offriends of a user of a client device 102; the retrieval of messages andcontent; the adding and deleting of friends to a social graph; thelocation of friends within a social graph; access to user conversationdata; access to avatar information stored on messaging server system108; and opening an application event (e.g., relating to the messagingclient application 104).

The application server 112 hosts a number of applications andsubsystems, including a messaging server application 114, an imageprocessing system 116, a social network system 122, and the hand shapeand pose estimation system 124. The messaging server application 114implements a number of message processing technologies and functions,particularly related to the aggregation and other processing of content(e.g., textual and multimedia content) included in messages receivedfrom multiple instances of the messaging client application 104. As willbe described in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available, by themessaging server application 114, to the messaging client application104. Other processor- and memory-intensive processing of data may alsobe performed server-side by the messaging server application 114, inview of the hardware requirements for such processing.

The application server 112 also includes an image processing system 116that is dedicated to performing various image processing operations,typically with respect to images or video received within the payload ofa message at the messaging server application 114. A portion of theimage processing system 116 may also be implemented by the hand shapeand pose estimation system 124.

The social network system 122 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph within the database120. Examples of functions and services supported by the social networksystem 122 include the identification of other users of the messagingsystem 100 with which a particular user has relationships or is“following” and also the identification of other entities and interestsof a particular user. Such other users may be referred to as the user'sfriends.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is a schematic diagram 200 illustrating data, which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table214. An entity table 202 stores entity data, including an entity graph204. Entities for which records are maintained within the entity table202 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 204 furthermore stores information regardingrelationships and associations between entities. Such relationships maybe social, professional (e.g., work at a common corporation ororganization), interest-based, or activity-based, merely for example.

Message table 214 may store a collection of conversations between a userand one or more friends or entities. Message table 214 may includevarious attributes of each conversation, such as the list ofparticipants, the size of the conversation (e.g., number of users and/ornumber of messages), the chat color of the conversation, a uniqueidentifier for the conversation, and any other conversation relatedfeature(s). Information from message table 214 may be provided inlimited form and on a limited basis to a given web-based gamingapplication based on functions of the messaging client application 104invoked by the web-based gaming application.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 212. Database 120 also stores annotatedcontent received in the annotation table 212. Filters for which data isstored within the annotation table 212 are associated with and appliedto videos (for which data is stored in a video table 210) and/or images(for which data is stored in an image table 208). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a UI by the messaging client application 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102. Another type of filter is a data filter,which may be selectively presented to a sending user by the messagingclient application 104, based on other inputs or information gathered bythe client device 102 during the message creation process. Examples ofdata filters include current temperature at a specific location, acurrent speed at which a sending user is traveling, battery life for aclient device 102, or the current time.

Other annotation data that may be stored within the image table 208 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 210 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 214. Similarly, the image table 208 storesimage data associated with messages for which message data is stored inthe entity table 202. The entity table 202 may associate variousannotations from the annotation table 212 with various images and videosstored in the image table 208 and the video table 210.

Trained machine learning technique(s) 207 stores parameters that havebeen trained in the first and second training phases of the hand shapeand pose estimation system 124. For example, trained machine learningtechniques 207 stores the trained parameters of a stacked hourglassnetwork, a residual network, a graph CNN, a 3D pose regressor, and meshrenderer machine learning techniques.

Synthetic and real hand training images 209 stores a first plurality ofimages of depictions of a computer-generated hand and a second pluralityof depictions of real-world hands. The first plurality of images storedin the synthetic and real hand training images 209 includes variousground truth information (e.g., 3D pose, 3D mesh and/or shape groundtruth information) for each image in the first plurality of images. Thisfirst plurality of images is used by the hand shape and pose estimationsystem 124 in a first training phase to train the machine learningtechniques. The second plurality of images stored in the synthetic andreal hand training images 209 includes various depictions of areal-world hand together with 3D depth information captured from a 3Ddepth sensor for each image in the second plurality of images. Thissecond plurality of images is used by the hand shape and pose estimationsystem 124 in a second training phase to train the machine learningtechniques.

In some implementations, the first plurality of images (also referred toas synthetic images), stored in synthetic and real hand training images209, provides the labels of both 3D hand joint locations and full 3Dhand meshes. A 3D hand model is generated, rigged with joints, and thenphotorealistic textures are applied on the 3D hand model as well asnatural lighting using high-dynamic range (HDR) images. The variationsof the hand are modeled by creating blend shapes with different shapesand ratios, and then random weights are applied to the blend shapes.Hand poses from 500 common hand gestures and 1000 unique cameraviewpoints are created and captured in the first plurality of images. Tosimulate real-world diversity, 30 lightings and five skin colors areused. The hand is rendered using global illumination. In someimplementations, the first plurality of images includes 375,000 hand RGBimages with large variations. In some embodiments, only a portion (e.g.,315,000) of the first plurality of images are used in the first trainingphase to train the machine learning techniques. During training orbefore, each rendered hand in the first plurality of images is croppedfrom the image and blended with a randomly selected background image(e.g., a city image, a living room image, or any other suitable imageobtained randomly or pseudo-randomly from a background image server(s)).To do this, the system obtains an image that contains a rendered 3D handmesh, the 3D hand mesh is cropped and extracted from the image, abackground image is randomly selected, and the cropped 3D hand mesh iscombined with the selected background image and stored as a new image tobe used in the first training phase. Particularly, the first pluralityof images used to train the machine learning techniques in the firsttraining phase is modified to include a variety of simulated orcomputer-generated hand models overlaid on top of or blended with abackground image to provide a more realistic representation.

In some implementations, the second plurality of images, stored insynthetic and real hand training images 209, includes hand RGB imageswith the corresponding depth images of each RGB image captured using aRGB-D camera (e.g., an RGB camera fitted with a depth sensor or depthcamera).

A story table 206 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 202). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the UI of themessaging client application 104 may include an icon that isuser-selectable to enable a sending user to add specific content to hisor her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via a UTof the messaging client application 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client application 104 based on his or her location. Theend result is a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 3 is a schematic diagram illustrating a structure of a message 300,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 300 is used to populate the message table 214stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 300 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 300 is shown to include thefollowing components:

-   -   A message identifier 302: a unique identifier that identifies        the message 300.    -   A message text payload 304: text, to be generated by a user via        a UI of the client device 102 and that is included in the        message 300.    -   A message image payload 306: image data, captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 300.    -   A message video payload 308: video data, captured by a camera        component or retrieved from a memory component of the client        device 102 and that is included in the message 300.    -   A message audio payload 310: audio data, captured by a        microphone or retrieved from the memory component of the client        device 102, and that is included in the message 300.    -   A message annotations 312: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to message image payload 306, message video payload        308, or message audio payload 310 of the message 300.    -   A message duration parameter 314: parameter value indicating, in        seconds, the amount of time for which content of the message        (e.g., the message image payload 306, message video payload 308,        message audio payload 310) is to be presented or made accessible        to a user via the messaging client application 104.    -   A message geolocation parameter 316: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message. Multiple message geolocation        parameter 316 values may be included in the payload, with each        of these parameter values being associated with respect to        content items included in the content (e.g., a specific image        within the message image payload 306, or a specific video in the        message video payload 308).    -   A message story identifier 318: identifier value identifying one        or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 306 of the        message 300 is associated. For example, multiple images within        the message image payload 306 may each be associated with        multiple content collections using identifier values.    -   A message tag 320: each message 300 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 306        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 320 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   A message sender identifier 322: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 300 was generated and from which the message        300 was sent.    -   A message receiver identifier 324: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of user(s) of the client device 102 to        which the message 300 is addressed. In the case of a        conversation between multiple users, the identifier may indicate        each user involved in the conversation.

The contents (e.g., values) of the various components of message 300 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 306 maybe a pointer to (or address of) a location within an image table 208.Similarly, values within the message video payload 308 may point to datastored within a video table 210, values stored within the messageannotations 312 may point to data stored in an annotation table 212,values stored within the message story identifier 318 may point to datastored in a story table 206, and values stored within the message senderidentifier 322 and the message receiver identifier 324 may point to userrecords stored within an entity table 202.

FIG. 4 is a block diagram showing an example hand shape and poseestimation system 124, according to example embodiments. Hand shape andpose estimation system 124 operates on a set of input data (e.g.,monocular image depicting a real-world hand 401, synthetic hand trainingimage data 402, and real-world hand training image data 403). The set ofinput data is obtained from synthetic and real hand training images 209stored in database(s) 200 during the training phases and is obtainedfrom an RGB camera of a client device 102 when a AR/VR application 105is being used. Hand shape and pose estimation system 124 includes afirst machine learning technique module 412, a heat map loss module 414,a second machine learning technique module 416, a graph CNN module 418,a hand mesh loss module 420, a pose regressor module 430, pose lossmodule 432, a hand mesh rendering module 440, and a depth map lossmodule 450.

In general, according to the disclosed embodiments, hand shape and poseestimation system 124 receives an input image (e.g., monocular imagedepicting a real-world hand 401) as a single RGB image centered on ahand. This image is passed through a two-stacked hourglass network toinfer 2D heat-maps. The estimated 2D heat-maps, combined with imagefeature maps, are encoded as a latent feature vector using a residualnetwork that contains eight residual layers and four max pooling layers.The encoded latent feature vector is then input to a graph CNN to inferthe 3D coordinates of N vertices V={v_(i)}_(i=1) ^(N) in the 3D handmesh. The 3D hand joint locations Φ={ϕ_(j)}_(j=1) ^(J) are linearlyregressed from the reconstructed 3D hand mesh vertices by using asimplified linear graph CNN.

In some implementations, the first machine learning technique module 412includes a stacked hourglass network. The stacked hourglass network istrained to capture and consolidate information across all scales of animage. The network pools pixels of the image and subsequently upsamplesthe image to get the final output. Specifically, the hourglass networkpools down the image to a very low resolution, then upsamples andcombines features across multiple resolutions. Multiple hourglassmodules can be strung together or stacked, allowing for repeatedbottom-up, top-down inference across scales. In some embodiments, atwo-stacked hourglass network is used as the first machine learningtechnique module 412.

The first machine learning technique module 412 is configured toestimate a heat map based on the input image. The first machine learningtechnique module 412 provides the generated heat map to the heat maploss module 414 to compute a heat map loss with respect to the groundtruth heat map (in training) or a precomputed and pretrainedcoefficient. The first machine learning technique module 412 is trainedin a training phase based on the output of the heat map loss module 414.In an implementation, the heat map loss module 414 computes the heat-maploss in accordance with the function:

=Σ_(j=1) ^(J)∥

_(j)−

_(j)∥₂ ²,  (1)where H_(j) and

_(j) are the ground truth and estimated heat-maps, respectively. In anembodiment, the heat-map resolution is set as 64×64 pixels. The groundtruth heat-map is defined as a 2D Gaussian with a standard deviation of4 pixels centered on the ground truth 2D joint location.

In some implementations, the second machine learning technique module416 includes a residual network (a type of artificial neural network).In an implementation, the residual network contains eight residuallayers and four max pooling layers. The residual network can be trainedusing residual learning implemented to every few stacked layers. As anexample, formulation (1) can be defined as:F(x)=W2σ(W1x)+x  (2)where W1 and W2 are the weights for the convolutional layers and σ isthe activation function. The operation F+x is realized by a shortcutconnection and element-wise addition. The addition is followed by anactivation function σ. The resulting formulation for a residual blockis:y(x)=σ(W2σ(W1x)+x).  (3)

After each convolution (weight) layer, a batch normalization method (BN)is adopted. The training of the network is achieved by stochasticgradient descent (SGD) with a mini-batch size of 256. The learning ratestarts from 0.1 and is divided by 10 when the error plateaus. The weightdecay rate is 0.0001 and has a value of 0.9.

The output of the second machine learning technique module 416 encodesthe estimated 2D heat maps from the first machine learning techniquemodule 412 and the image feature maps as a latent feature vector. Thislatent feature vector is provided to the graph CNN module 418.

Graph CNN module 418 implements a CNN specifically designed to operateon a graph-based vector of information. Generally, CNN is a type offeed-forward artificial neural network where the individual neurons aretiled in such a way that they respond to overlapping regions in thevisual field. CNNs consist of multiple layers of small neuroncollections, which look at small portions of the input image, calledreceptive fields. The results of these collections are then tiled sothat they overlap to obtain a better representation of the originalimage; this is repeated for every such layer. Convolutional networks mayinclude local or global pooling layers, which combine the outputs ofneuron clusters. They also consist of various combinations ofconvolutional layers and fully connected layers, with pointwisenonlinearity applied at the end of or after each layer. To avoid thesituation that there exist billions of parameters if all layers arefully connected, the idea of using a convolution operation on smallregions has been introduced. One major benefit of convolutional networksis the use of shared weight in convolutional layers, which means thatthe same filter (weights bank) is used for each pixel in the layer; thisboth reduces required memory size and improves performance.

SVMs are supervised learning models with associated learning algorithmsthat are configured to recognize patterns. Given a set of trainingexamples, with each marked as belonging to one of two categories, an SVMtraining algorithm builds a model that assigns new examples into onecategory or the other, making it a non-probabilistic binary linearclassifier. An SVM model is a representation of the examples as pointsin space, mapped so that the examples of the separate categories aredivided by a clear gap that is as wide as possible. New examples arethen mapped into that same space and predicted to belong to a categorybased on which side of the gap they fall on.

An embodiment of the graph CNN module 418 is shown in FIG. 5.Particularly, the graph CNN module 418 generates 3D coordinates ofvertices in the hand mesh and estimates the 3D hand pose from the mesh.In this way, the graph CNN module 418 models, based on featuresextracted by other machine learning technique modules of FIG. 5, a postof a hand depicted in a monocular image by adjusting skeletal jointpositions of a 3D hand mesh and also models a shape of the hand in themonocular image by adjusting blend shape values of the 3D hand meshrepresenting surface features of the hand depicted in the monocularimage. The resulting 3D hand mesh is then generated for display. Anillustrative 3D mesh 803 with its different viewpoints 804 is shown inFIG. 8. A 3D mesh can be represented by an undirected graph:M=(V,ε,W), where V={v _(i)}_(i=1) ^(N)  (4)is the set of N vertices in the mesh, where according to Equations 5 and6:ε={e _(i)}_(i=1) ^(E) is a set of E edges in the mesh,  (5)W=(w _(ij))_(N×N) is the adjacency matrix,  (6)where w_(ij)=0 if (i,j)∈ε. The graph Laplacian is computed as L=D−W,where Equation 7:D=diag(Σ_(j) w _(ij))  (7)is the diagonal degree matrix. The topology may be a triangular meshthat is fixed and is predefined by the hand mesh model (e.g., theadjacency matrix W and the graph Laplacian L of the graph M are fixedduring training).

Given a signal, according to Equation 8:f={tilde over (()}ƒ₁, . . . ,ƒ_(N){tilde over ())}^(T)∈

^(N×F)  (8)on the vertices of graph M, F-dim features of N vertices are representedin the 3D mesh. The graph convolutional operation on the graph signal,according to Equation 9:f _(in)∈

^(N×F) ^(in) is defined as f _(out)=Σ_(k=0) ^(K-1) T _(k)({tilde over(L)})·f _(in)·θ_(k),  (9)where according to Equation 10:T _(k)(x)=2xT _(k-1)(x)−T _(k-2)(x)  (10)is a polynomial of degree k, T₀=1, T₁=x, {tilde over (L)}∈

^(N×N) is the rescaled Laplacian, θ_(k)∈

^(F) ^(in) ^(×F) ^(out) the trainable parameters in the graphconvolutional layer, f_(out)∈

^(N×F) ^(out) is the output graph signal of the graph CNN module 418.

In some embodiments, a hierarchical architecture for mesh generation isprovided by performing graph convolution on graphs from coarse to fine,as shown in FIG. 5. The topologies of coarse graphs are precomputed bygraph coarsening as shown in process 501 and are fixed during training.A multilevel clustering process is used to coarsen the graph and createa tree structure to store correspondences of vertices in graphs asadjacent coarsening levels. During the forward propagation, features ofvertices 510 in the coarse graph are upsampled to corresponding childrenvertices 520 in the fine graph, as shown in process 502. The graphconvolution is then performed to update features in the graph. All thegraph convolutional filters have the same support of K=3. To make thenetwork output independent of the camera intrinsic parameters, thenetwork is configured to output UV coordinates on input image and depthvertices in the mesh, which can be converted to 3D coordinates in thecamera coordinate system using the camera intrinsic matrix.Scale-invariant and root-relative depth of mesh vertices are estimated.Considering the 3D joint locations can be estimated directly from the 3Dmesh vertices using a linear regressor, a simplified graph CNN can beimplemented with two pooling layers and without nonlinear activation tolinearly regress the scale-invariant and root-relative 3D hand jointlocations from 3D coordinates of hand mesh vertices.

Referring back to FIG. 4, the output hand mesh that is estimated by thegraph CNN module 418 is provided to hand mesh loss module 420. The handmesh loss module 420 computes hand mesh loss by comparing the estimatedhand mesh received from graph CNN module 418 with respect to the groundtruth hand mesh (in the first training phase training), thepseudo-ground truth hand mesh (in the second training phase), or aprecomputed and pretrained coefficient. The graph CNN module 418 istrained in a training phase based on the output of the hand mesh lossmodule 420. In an implementation, the hand mesh loss module 420 computesthe hand mesh loss in accordance with Equation 11:

=λ_(v)

_(v)+λ_(n)

_(n)+λ_(e)

_(e)+λ_(l)

_(l)  (11)which is composed of vertex loss

_(v), normal loss

_(n), edge loss

_(e), and Laplacian loss

_(l). The vertex loss

_(v) is to constrain 2D and 3D locations of mesh vertices according toEquation 12:

_(n)=Σ_(i=1) ^(N) ∥v _(i) ^(3D) −{circumflex over (v)} _(j) ^(3D)∥₂ ²+∥v _(i) ^(2D) −{circumflex over (v)} _(i) ^(2D)∥₂ ²,  (12)where v_(i) and {circumflex over (v)}_(i) denote the ground truth andestimated 2D/3D locations of the mesh vertices, respectively. The normalloss

_(n) is to enforce surface normal consistency according to Equation 13:

_(n)=Σ_(t)Σ_((i,j)∈t)∥

{circumflex over (v)}i^(3D) −{circumflex over (v)} _(j) ^(3D) ,n _(t)

∥₂ ²,  (13)the index of triangle faces in the mesh, (i,j) are the indices thatcompose one edge of triangle t, and n_(t) is the ground truth normalvector of triangle face t. The edge loss

_(e) is introduced to enforce edge length consistency according toEquation 14:

_(e)=Σ_(i=1) ^(E)(∥e _(i)∥₂ ² −∥ê _(i)∥₂ ²)²,  (14)where e_(i) and ê_(i) denote the ground truth and estimated edgevectors, respectively. The Laplacian loss

_(l) is introduced to preserve the local surface smoothness of the meshaccording to Equation 15:

=Σ_(i=1) ^(N)∥δ_(i)−

δ_(k) /B _(i)∥₂ ²,  (15)where δ_(i)=v_(i) ^(3D)−{circumflex over (v)}_(i) ^(3D) is the offsetfrom the estimation to the ground truth, N(v_(i)) is the set ofneighboring vertices of v_(i), and B_(i) is the number of vertices inthe set N(v_(i)). This loss function prevents the neighboring verticesfrom having opposite offsets, making the estimated 3D hand surface meshsmoother. In some implementations, λ_(v)=1, λ_(n)=1, λ_(e)=1, λ_(l)=50.

Pose regressor module 430 implements a regressor neural network (amachine learning technique) and is configured to process the hand meshgenerated by graph CNN module 418 to generate a 3D pose graph. Anillustrative pose graph 805 is shown in FIG. 8. The output 3D pose graphthat is estimated by the pose regressor module 430 is provided to poseloss module 432. The pose loss module 432 computes a pose loss inaccordance with Equation 16:

=Σ_(j=1) ^(J)∥ϕ_(j) ^(3D)−{circumflex over (ϕ)}_(j) ^(3D)∥₂ ²,  (16)where ϕ_(j) ^(3D) are {circumflex over (ϕ)}_(j) ^(3D) the ground truthand estimated 3D joint locations, respectively.

In some implementations, in a first training phase, synthetic handtraining image data 402 is used to train the stacked hourglass network(e.g., first machine learning technique module 412) and the 3D poseregressor (e.g., another machine learning technique) separately with theheat-map loss module 414 and the 3D pose loss module 432, respectively.Subsequently, the stacked hourglass network, the residual network (e.g.,the second machine learning technique module 416), and the graph CNNmodule 418 are trained for mesh generation with the combined lossfunction of Equation 17:

_(fully)=

+

+

.  (17)In some implementations,

=0.5,

=1,

=1. Particularly, in the first training phase, each of the loss modules414, 420 and 432 is provided with ground truth information correspondingto a given image from synthetic hand training image data 402 beingprocessed. The first machine learning technique module 412 processes thegiven image from synthetic hand training image data 402 to generate anestimated heat map from the image, the graph CNN module 418 processesthe given image to estimate a hand mesh, and the pose regressor module430 processes the given image to estimate a 3D pose graph. Each of thesenetworks is trained to minimize the corresponding loss of loss modules414, 420 and 432.

After the networks are trained in the first training phase, in a secondtraining phase, real hand training image data 403 is used to train themachine learning techniques network 410. Specifically, the networks arefine-tuned in a weakly-supervised manner. In some implementations, thenetworks are trained without the ground truth of 3D joint locations. Areference depth map is leveraged corresponding to the received imagesdepicting the real-world hand to employ a differentiable renderer torender the estimated 3D hand mesh to a depth map from the cameraviewpoint. This is the function of hand mesh rendering module 440. Theoutput of the hand mesh rendering module 440 is provided to depth maploss module 450 to compare to the ground truth depth map. Specifically,the smooth L1 loss is used for the depth map loss, which is computed inaccordance with Equation 20:

=smooth_(L1)(D,{circumflex over (D)}),

=

(

),  (20)where

and

denote the ground truth and the rendered depth maps, respectively.

(⋅) is the depth rendering function; and

is the estimated 3D hand mesh. In some implementations, the resolutionof the depth map is 32×32 pixels.

In some cases, training in the second phase with only the depth map losscould lead to a degenerated solution since the depth map loss onlyconstrains the visible surface and is sensitive to the noise in thecaptured depth map. To address this issue, a pseudo-ground truth mesh

is created using the pretrained models from the first phase and theground truth heat maps. The pseudo-ground truth mesh

has a reasonable edge length and good surface smoothness. In animplementation, an edge loss

is adopted and Laplacian loss

as the pseudo-ground truth mesh loss according to Equation 21:

=λ_(e)

+λ_(l)

, where λ_(e)=1,λ_(i)=50.  (21)in order to preserve the edge length and surface smoothness of the mesh.Specifically, this mesh loss is used by the hand mesh loss module 420 inthe second training phase. With the supervision of the pseudo-groundtruth meshes, the network can generate meshes with correct shape andsmooth surface. In an implementation, in the second training phase, thestacked hourglass network (e.g., first machine learning technique module412) is fine-tuned first with the heat-map loss and then all thenetworks are fine-tuned with the combined loss according to Equation 22:

_(weakly)=

+

+

,  (22)where

=0.1,

=0.1,

=1. In some embodiments, this loss function is performed on the datasetwithout 3D pose supervision. In some embodiments, when the ground truthof 3D joint locations is provided during training, the 3D pose loss

is added to the loss function

_(weakly) and the weight is set as

=10.

FIGS. 6-7 are flowcharts illustrating example operations of the handshape and pose estimation system 124 in performing processes 600-700,according to example embodiments. The processes 600-700 may be embodiedin computer-readable instructions for execution by one or moreprocessors such that the operations of the processes 600-700 may beperformed in part or in whole by the functional components of themessaging server system 108 and/or AR/VR application 105; accordingly,the processes 600-700 are described below by way of example withreference thereto. However, in other embodiments, at least some of theoperations of the processes 600-700 may be deployed on various otherhardware configurations. The processes 600-700 are therefore notintended to be limited to the messaging server system 108 and can beimplemented in whole, or in part, by any other component.

At operation 601, the hand shape and pose estimation system 124 receivesa monocular image that includes a depiction of a hand. For example, atrained machine learning techniques network 410 receives, from a clientdevice 102, an RGB image of a user's hand. This information is receivedand processed as monocular image depicting a hand 401.

At operation 602, the hand shape and pose estimation system 124 extractsone or more features of the monocular image using a plurality of machinelearning techniques. For example, the first machine learning techniquemodule 412 estimates a heat map of the image, a second machine learningtechnique module 416 encodes the estimated heat map and image featuremaps using a residual network, and a pose regressor module 430 estimatesa 3D pose graph from a mesh representing the hand depicted in the image.At operation 603, the hand shape and pose estimation system 124 models,based on the extracted one or more features, a pose of the hand depictedin the monocular image by adjusting skeletal joint positions of a 3Dhand mesh using a trained graph CNN. For example, the graph CNN module418 generates a 3D hand mesh using the heat map estimated by the firstmachine learning technique module 412 which identifies skeletal jointpositions. To do this, graph CNN module 418 receives latent featuresfrom the second machine learning technique module 416 and performs graphconvolutional operations in accordance with Equation 9 to adjustskeletal joint positions of the 3D hand mesh.

At operation 604, the hand shape and pose estimation system 124 models,based on the extracted one or more features, a shape of the handdepicted in the monocular image by adjusting blend shape values of the3D hand mesh representing surface features of the hand using a trainedgraph CNN. For example, the graph CNN module 418 infers the 3Dcoordinates of the 3D hand mesh and linearly regresses the 3D hand jointlocations from the reconstructed 3D hand mesh vertices.

At operation 605, the hand shape and pose estimation system 124generates, for display, the 3D hand mesh adjusted to model the pose andshape of the hand depicted in the monocular image. For example, a 3Dmesh 803 (FIG. 8) is adjusted to model the pose (e.g., using 3D posegraph 805) and shape of a hand depicted in given hand training imagedata 801. This 3D mesh 803 and 3D pose graph 805 are returned to clientdevice 102 for presentation in the AR/VR application 105.

FIG. 7 illustrates a process 700 for training one or more machinelearning techniques to generate a 3D hand mesh representing the pose andshape of a hand depicted in a monocular image (e.g., an RGB image). Theprocess 700 includes a set of operations performed in a first trainingphase 710 and a set of operations performed in a second training phase720 that follows the first training phase 710.

At operation 701, the hand shape and pose estimation system 124 obtainsa first plurality of input images that include synthetic representationsof a hand. For example, machine learning techniques network 410initially receives synthetic hand training image data 402. Anillustrative synthetic hand training image data 801 and itscorresponding output is shown in a first row 810 of FIG. 8.

At operation 702, the hand shape and pose estimation system 124initially trains a first machine learning technique based on a firstattribute of the first plurality of images. For example, for a givenimage from the synthetic hand training image data 402, ground truth heatmap information is obtained and provided to heat map loss module 414.The first machine learning technique module 412 estimates a heat map forthe given image (e.g., using a stacked hourglass network) and is trainedto minimize the loss computed by heat map loss module 414.

At operation 703, the hand shape and pose estimation system 124initially trains a second machine learning technique based on a secondfeature of the first plurality of images separately from training thefirst machine learning technique. For example, for a given image fromthe synthetic hand training image data 402, ground truth poseinformation is obtained and provided to pose loss module 432. The poseregressor module 430 estimates a pose for the given image (e.g., using adifferentiable renderer network) and is trained to minimize the losscomputed by pose loss module 432.

At operation 704, the hand shape and pose estimation system 124 trainsthe first and second machine learning techniques together with a graphCNN based on the first plurality of input images. For example, for agiven image from the synthetic hand training image data 402, groundtruth hand mesh information is obtained and provided to hand mesh lossmodule 420. The graph CNN module 418 estimates a hand mesh for the givenimage and is trained to minimize the loss computed by hand mesh lossmodule 420. The machine learning techniques network 410 are trained tominimize the loss together in accordance with Equation 17.

At operation 705, the hand shape and pose estimation system 124 obtainsa second plurality of input images that include real-world depictions ofa hand and reference 3D depth maps captured using a depth camera. Forexample, machine learning techniques network 410 receives real handtraining image data 403. An illustrative synthetic hand training imagedata 801 and its corresponding output is shown in a second row 820 ofFIG. 8.

At operation 706, the hand shape and pose estimation system 124generates a pseudo-ground truth mesh of the real-world depictions of thehand using the graph CNN trained in the first training phase. Forexample, a pseudo-

ground truth mesh is created using the pretrained models from the firsttraining phase and the ground truth heat-maps of the real hand trainingimage data 403.

At operation 707, the hand shape and pose estimation system 124 trainsthe first and second machine learning techniques together with the graphCNN based on the pseudo-ground truth mesh, the real-world depictions ofthe hand, and the reference 3D depth maps. For example, the machinelearning techniques network 410 are trained to minimize the losstogether in accordance with Equation 22 or also considering 3D pose loss

.

As discussed above, FIG. 8 provides example outputs of the hand shapeand pose estimation system 124 as applied to synthetic data shown in row810 and as applied to real-world data shown in row 820. Specifically,the hand shape and pose estimation system 124 generates an output 2Dpost 802 (e.g., using first machine learning technique module 412) givencorresponding to the input data, an output 3D mesh 803 (e.g., usinggraph CNN module 418), and an output 3D pose graph 805 (e.g., using poseregressor module 430).

FIG. 9 is a block diagram illustrating an example software architecture906, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 9 is a non-limiting example of asoftware architecture and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 906 may execute on hardwaresuch as machine 1000 of FIG. 10 that includes, among other things,processors 1004, memory 1014, and input/output (I/O) components 1018. Arepresentative hardware layer 952 is illustrated and can represent, forexample, the machine 1000 of FIG. 10. The representative hardware layer952 includes a processing unit 954 having associated executableinstructions 904. Executable instructions 904 represent the executableinstructions of the software architecture 906, including implementationof the methods, components, and so forth described herein. The hardwarelayer 952 also includes memory and/or storage modules memory/storage956, which also have executable instructions 904. The hardware layer 952may also comprise other hardware 958.

In the example architecture of FIG. 9, the software architecture 906 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 906 mayinclude layers such as an operating system 902, libraries 920,frameworks/middleware 918, applications 916, and a presentation layer914. Operationally, the applications 916 and/or other components withinthe layers may invoke API calls 908 through the software stack andreceive messages 912 in response to the API calls 908. The layersillustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile or specialpurpose operating systems may not provide a frameworks/middleware 918,while others may provide such a layer. Other software architectures mayinclude additional or different layers.

The operating system 902 may manage hardware resources and providecommon services. The operating system 902 may include, for example, akernel 922, services 924, and drivers 926. The kernel 922 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 922 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 924 may provideother common services for the other software layers. The drivers 926 areresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers 926 include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 920 provide a common infrastructure that is used by theapplications 916 and/or other components and/or layers. The libraries920 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 902 functionality (e.g., kernel 922,services 924 and/or drivers 926). The libraries 920 may include systemlibraries 944 (e.g., C standard library) that may provide functions suchas memory allocation functions, string manipulation functions,mathematical functions, and the like. In addition, the libraries 920 mayinclude API libraries 946 such as media libraries (e.g., libraries tosupport presentation and manipulation of various media format such asMPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., anOpenGL framework that may be used to render two-dimensional andthree-dimensional in a graphic content on a display), database libraries(e.g., SQLite that may provide various relational database functions),web libraries (e.g., WebKit that may provide web browsingfunctionality), and the like. The libraries 920 may also include a widevariety of other libraries 948 to provide many other APIs to theapplications 916 and other software components/modules.

The frameworks/middleware 918 (also sometimes referred to as middleware)provide a higher-level common infrastructure that may be used by theapplications 916 and/or other software components/modules. For example,the frameworks/middleware 918 may provide various graphic UI (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks/middleware 918 may provide a broad spectrumof other APIs that may be utilized by the applications 916 and/or othersoftware components/modules, some of which may be specific to aparticular operating system 902 or platform.

The applications 916 include built-in applications 938 and/orthird-party applications 940. Examples of representative built-inapplications 938 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 940 may include anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™ ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 940 may invoke the API calls 908 provided bythe mobile operating system (such as operating system 902) to facilitatefunctionality described herein.

The applications 916 may use built-in operating system functions (e.g.,kernel 922, services 924, and/or drivers 926), libraries 920, andframeworks/middleware 918 to create UIs to interact with users of thesystem. Alternatively, or additionally, in some systems, interactionswith a user may occur through a presentation layer, such as presentationlayer 914. In these systems, the application/component “logic” can beseparated from the aspects of the application/component that interactwith a user.

FIG. 10 is a block diagram illustrating components of a machine 1000,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1010 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1010 may be used to implement modules or componentsdescribed herein. The instructions 1010 transform the general,non-programmed machine 1000 into a particular machine 1000 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1000 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1000 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1000 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1010, sequentially or otherwise, that specify actions to betaken by machine 1000. Further, while only a single machine 1000 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1010 to perform any one or more of the methodologiesdiscussed herein.

The machine 1000 may include processors 1004, memory/storage 1006, andI/O components 1018, which may be configured to communicate with eachother such as via a bus 1002. In an example embodiment, the processors1004 (e.g., a central processing unit (CPU), a reduced instruction setcomputing (RISC) processor, a complex instruction set computing (CISC)processor, a graphics processing unit (GPU), a digital signal processor(DSP), an application-specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, a processor 1008and a processor 1012 that may execute the instructions 1010. The term“processor” is intended to include multi-core processors 1004 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.10 shows multiple processors 1004, the machine 1000 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiple cores, or any combination thereof.

The memory/storage 1006 may include a memory 1014, such as a mainmemory, or other memory storage, and a storage unit 1016, bothaccessible to the processors 1004 such as via the bus 1002. The storageunit 1016 and memory 1014 store the instructions 1010 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1010 may also reside, completely or partially, within thememory 1014, within the storage unit 1016, within at least one of theprocessors 1004 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1000. Accordingly, the memory 1014, the storage unit 1016, and thememory of processors 1004 are examples of machine-readable media.

The I/O components 1018 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1018 that are included in a particular machine 1000 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1018 may include many other components that are not shown inFIG. 10. The I/O components 1018 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1018may include output components 1026 and input components 1028. The outputcomponents 1026 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1028 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1018 may includebiometric components 1030, motion components 1034, environmentalcomponents 1036, or position components 1038 among a wide array of othercomponents. For example, the biometric components 1030 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1034 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1036 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1038 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1018 may include communication components 1040operable to couple the machine 1000 to a network 1032 or devices 1020via coupling 1024 and coupling 1022, respectively. For example, thecommunication components 1040 may include a network interface componentor other suitable device to interface with the network 1032. In furtherexamples, communication components 1040 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1020 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 1040 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1040 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1040, such as, location via Internet Protocol (P) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting a NFC beaconsignal that may indicate a particular location, and so forth.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying transitory or non-transitoryinstructions for execution by the machine, and includes digital oranalog communications signals or other intangible medium to facilitatecommunication of such instructions. Instructions may be transmitted orreceived over the network using a transitory or non-transitorytransmission medium via a network interface device and using any one ofa number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, PDAs, smartphones, tablets, ultra books, netbooks, laptops, multi-processorsystems, microprocessor-based or programmable consumer electronics, gameconsoles, set-top boxes, or any other communication device that a usermay use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“EPHEMERAL MESSAGE” in this context refers to a message that isaccessible for a time-limited duration. An ephemeral message may be atext, an image, a video, and the like. The access time for the ephemeralmessage may be set by the message sender. Alternatively, the access timemay be a default setting or a setting specified by the recipient.Regardless of the setting technique, the message is transitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, device,or other tangible media able to store instructions and data temporarilyor permanently and may include, but is not limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-readable medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“COMPONENT” in this context refers to a device, physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an ASIC. A hardware componentmay also include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware component may include software executed by a general-purposeprocessor or other programmable processor. Once configured by suchsoftware, hardware components become specific machines (or specificcomponents of a machine) uniquely tailored to perform the configuredfunctions and are no longer general-purpose processors. It will beappreciated that the decision to implement a hardware componentmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations. Accordingly, the phrase“hardware component” (or “hardware-implemented component”) should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. Consideringembodiments in which hardware components are temporarily configured(e.g., programmed), each of the hardware components need not beconfigured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output.

Hardware components may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,”, etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-FrequencyIntegrated Circuit (RFIC) or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

Changes and modifications may be made to the disclosed embodimentswithout departing from the scope of the present disclosure. These andother changes or modifications are intended to be included within thescope of the present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: receiving, by one or moreprocessors, a monocular image that includes a depiction of a hand;modeling, by the one or more processors, a pose of the hand depicted inthe monocular image by adjusting skeletal joint positions of athree-dimensional (3D) hand mesh using a trained graph convolutionalneural network (CNN), the trained graph CNN estimating 3D coordinates ofvertices in the 3D hand mesh; linearly regressing the joint positionsusing a linear graph CNN; and generating, for display by the one or moreprocessors, the 3D hand mesh adjusted to model the pose of the handdepicted in the monocular image.
 2. The method of claim 1, furthercomprising: extracting one or more features of the monocular image usinga plurality of machine learning techniques, the extracting comprises:applying a first of the plurality of machine learning techniques to themonocular image to estimate a two-dimensional (2D) heat map of the handin the monocular image and to generate an image feature map; andencoding the 2D heat map and the image feature map using a second of theplurality of machine learning techniques to generate a feature vector,wherein the trained CNN is applied to the feature vector.
 3. The methodof claim 2, wherein the first machine learning technique comprises astacked hourglass network, and wherein the second machine learningtechnique comprises a residual network.
 4. The method of claim 1 furthercomprising: modeling based on one or more extracted features of themonocular image, a shape of the hand in the monocular image by adjustingblend shape values of the 3D hand mesh representing surface features ofthe hand depicted in the monocular image using the trained graph CNN. 5.The method of claim 4 further comprising: training a plurality ofmachine learning techniques and the graph CNN in first and secondtraining phases.
 6. The method of claim 5, wherein the first trainingphase comprises training the plurality of machine learning techniquesand the graph CNN based on a first plurality of input images thatinclude synthetic representations of a hand, ground truth 3D hand meshescorresponding to the synthetic representations of the hand, and 3D handjoint locations of the synthetic representations of the hand.
 7. Themethod of claim 6 further comprising generating an input image of thefirst plurality of input images by: generating a 3D hand model bycombining a plurality of hand joints with a plurality of surfacetextures; and combining the generated hand model with a backgroundimage.
 8. The method of claim 7 further comprising: randomly selecting ahand pose from a plurality of hand poses; adjusting the plurality ofhand joints based on the selected hand pose; and adjusting the pluralityof surface textures by applying random weights to blend shapes andratios.
 9. The method of claim 7, wherein generating the 3D hand modelcomprises: obtaining a 3D hand model that includes a first level ofcoarseness having a first number of vertices; applying the graph CNN tothe first level of coarseness; upsampling the 3D hand model to increasethe level of coarseness to a second level of coarseness having a secondnumber of vertices greater than the first number of vertices; generatinga tree structure representing correspondences of vertices in the firstand second levels of coarseness; and updating the graph CNN based on theupsampled 3D hand model and the generated tree structure.
 10. The methodof claim 6 further comprising: initially training a first of theplurality of machine learning techniques based on a first feature of thefirst plurality of input images; initially training a second of theplurality of machine learning techniques based on a second feature ofthe first plurality of input images separately from the first machinelearning technique; and after initially training the first and secondmachine learning techniques, training the first and second machinelearning techniques together with the graph CNN based on the firstplurality of input images.
 11. The method of claim 10, wherein initiallytraining the first machine learning technique comprises training thefirst machine learning technique based on a heat map loss function,wherein initially training the second machine learning techniquecomprises training the second machine learning technique based on a 3Dpose loss function, and wherein training the first and second machinelearning techniques together with the graph CNN comprises training thefirst and second machine learning techniques together based on the heatmap loss function, the 3D pose loss function, and a mesh loss function.12. The method of claim 6, wherein the second training phase isperformed following the first training phase, further comprising in thesecond training phase: receiving a second plurality of input images thatinclude real-world depictions of a hand and reference 3D depth maps ofthe real-world depictions of the hand captured using a depth camera;generating a pseudo-ground truth mesh of the real-world depictions ofthe hand using the graph CNN trained in the first phase; and trainingthe first and second machine learning techniques and the graph CNN basedon the generated pseudo-ground truth mesh, the real-world depictions ofthe hand, and the reference 3D depth maps of the real-world depictionsof the hand.
 13. The method of claim 12 further comprising: initiallytraining a first of the plurality of machine learning techniques basedon a heat map loss function and the second plurality of input images;and after initially training the first machine learning technique,training the first machine learning technique, a second machine learningtechnique, and the graph CNN based on the second plurality of inputimages, the reference 3D depth maps, the heat map loss function, a 3Dpose loss function, and a mesh loss function.
 14. The method of claim13, wherein the first machine learning technique comprises a stackedhourglass network and the second machine learning technique comprises adifferentiable renderer network.
 15. The method of claim 1, wherein themonocular image comprises a red, green, and blue (RGB) image withoutdepth information.
 16. The method of claim 1 further comprisingcontinuously changing an appearance of the 3D hand mesh by continuouslycapturing new monocular images of the hand in different positions,wherein the appearance of the 3D hand mesh changes to resemble thedifferent positions of the hand as the hand changes from one position toanother position.
 17. A system comprising: a processor configured toperform operations comprising: receiving a monocular image that includesa depiction of a hand; modeling a pose of the hand depicted in themonocular image by adjusting skeletal joint positions of athree-dimensional (3D) hand mesh using a trained graph convolutionalneural network (CNN), the trained graph CNN estimating 3D coordinates ofvertices in the 3D hand mesh; linearly regressing the joint positionsusing a linear graph CNN; and generating, for display, the 3D hand meshadjusted to model the pose of the hand depicted in the monocular image.18. The system of claim 17, wherein the operations further comprise:extracting one or more features of the monocular image using a pluralityof machine learning techniques, the extracting comprises: applying afirst of the plurality of machine learning techniques to the monocularimage to estimate a two-dimensional (2D) heat map of the hand in themonocular image and to generate an image feature map; and encoding the2D heat map and the image feature map using a second of the plurality ofmachine learning techniques to generate a feature vector, wherein thetrained CNN is applied to the feature vector.
 19. The system of claim18, wherein the first machine learning technique comprises a stackedhourglass network, and wherein the second machine learning techniquecomprises a residual network.
 20. A non-transitory machine-readablestorage medium that includes instructions that, when executed by one ormore processors of a machine, cause the machine to perform operationscomprising: receiving a monocular image that includes a depiction of ahand; modeling a pose of the hand depicted in the monocular image byadjusting skeletal joint positions of a three-dimensional (3D) hand meshusing a trained graph convolutional neural network (CNN), the trainedgraph CNN estimating 3D coordinates of vertices in the 3D hand mesh;linearly regressing the joint positions using a linear graph CNN; andgenerating, for display, the 3D hand mesh adjusted to model the pose ofthe hand depicted in the monocular image.